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London Clay, which underlies the majority of Greater London, has a high shrink–swell potential that can result in damage to foundations and surface infrastructure due to seasonal expansion and contraction of the clay. Currently, surface movement as a result of shrink–swell is not monitored in London, meaning that the magnitude and cyclicity of these movements is poorly understood. Persistent Scatterer Interferometric (PSI) Synthetic Aperture Radar data provide high-precision line-of-sight displacement measurements at a high point density across urban areas, offering the possibility of routine shrink–swell monitoring across whole cities. To test this, PSI data derived from TerraSAR-X (TSX) observations for the period from May 2011 to April 2017 were analysed for shrink–swell patterns across three areas of London in Hammersmith, Muswell Hill and Islington. A consistent cyclicity and amplitude was detected at all sites and the number of cycles is comparable with those identified in rainfall data. The amplitude of these cycles is smaller than anticipated, most probably because of the resisting effect of roads and pavements. The Cranfield University Leakage Assessment from Corrosivity and Shrinkage (LEACS) database was used to subdivide the PSI data and the average velocity and amplitude of each class statistically tested for significant differences between classes. The results show that it is not possible to statistically isolate possible soil shrink–swell movement in TSX PSI data in London. Thematic collection: This article is part of the Measurement and monitoring collection available at: https://www.lyellcollection.org/cc/measurement-and-monitoring [ABSTRACT FROM AUTHOR]

Design principles of a novel Multifunctional Operation Station (MOS) using Augmented Reality (AR) technology (MOSAR) are proposed in this paper. AR-based design allows more ergonomic remote instrument control in real time in contrast to classical instrument-centered interfaces. Another advantage is its hierarchical software structure including multiple programming interpreters. The MOSAR approach is illustrated with a remote surgical operating station that controls intelligent surgical instruments. The implementation of the Operation Station (MOS) is based on the multiplatform open-source library Tcl/Tk, and an AR extension has been developed on a Unity platform, using Vuforia SDK.

Reducing discolouration events in aging water supply networks is a major challenge facing water utilities worldwide as they are required to comply with increasingly stringent water quality regulations in a proactive and cost-effective manner. Emerging control methods for dynamic reconfiguration of network topologies could reduce the risk of discolouration by regulating hydrodynamic conditions to facilitate the implementation of self-cleaning networks. This review paper provides a holistic assessment of mechanisms and pathways which govern discolouration processes, associated models and the impact of system hydraulics so that control rules can be formulated. The possible role of biofilms in conditioning the wall-bound cohesive layers is identified and methods for their effective removal are discussed. The information presented identifies key variables and research gaps to facilitate the development of near real-time hydraulic and network topology control for the long-term and proactive management of discolouration risk. [ABSTRACT FROM AUTHOR]

The provision of self-cleaning velocities has been shown to reduce the risk of discolouration in water distribution networks (WDNs). Despite these findings, control implementations continue to be focused primarily on pressure and leakage management. This paper considers the control of diurnal flow velocities to maximize the self-cleaning capacity (SCC) of WDNs. We formulate a new optimal design-for-control problem where locations and operational settings of pressure control and automatic flushing valves are jointly optimized. The problem formulation includes a nonconvex objective function, nonconvex hydraulic conservation law constraints, and binary variables for modelling valve placement, resulting in a nonconvex mixed integer nonlinear programming (MINLP) optimization problem. Considering the challenges with solving nonconvex MINLP problems, we propose a heuristic algorithm which combines convex relaxations (with domain reduction), a randomization technique, and a multi-start strategy to compute feasible solutions. We evaluate the proposed algorithm on case study networks with varying size and degrees of complexity, including a large-scale operational network in the UK. The convex multi-start algorithm is shown to be a more robust solution method compared to an off-the-shelf genetic algorithm, finding good-quality feasible solutions to all design-for-control numerical experiments. Moreover, we demonstrate the implemented multi-start strategy to be a fast and scalable method for computing feasible solutions to the nonlinear SCC control problem. The proposed method extends the control capabilities and benefits of dynamically adaptive networks to improve water quality in WDNs., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Bradley Jenks, Filippo Pecci, and Ivan Stoianov report financial support was provided by Engineering and Physical Sciences Research Council. Bradley Jenks reports financial support was provided by Bristol Water Plc, Analytical Technology Inc, and Imperial College London’s Skempton Scholarship., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

We advance a novel computational theory of the hippocampal formation as a hierarchical generative model that organizes sequential experiences, such as rodent trajectories during spatial navigation, into coherent spatiotemporal contexts. We propose that the hippocampal generative model is endowed with inductive biases to identify individual items of experience (first hierarchical layer), organize them into sequences (second layer) and cluster them into maps (third layer). This theory entails a novel characterization of hippocampal reactivations as generative replay: the offline resampling of fictive sequences from the generative model, which supports the continual learning of multiple sequential experiences. We show that the model learns and efficiently retains multiple spatial navigation trajectories, by organizing them into spatial maps. Furthermore, the model reproduces flexible and prospective aspects of hippocampal dynamics that are challenging to explain within existing frameworks. This theory reconciles multiple roles of the hippocampal formation in map-based navigation, episodic memory and imagination., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

This paper investigates control and design-for-control strategies to improve the resilience of sectorized water distribution networks (WDN), while minimizing pressure induced pipe stress and leakage. Both evolutionary algorithms (EA) and gradient-based mathematical optimization approaches are investigated for the solution of the resulting large-scale non-linear (NLP) and bi-objective mixed-integer non-linear programs (BOMINLP). While EAs have been successfully applied to solve discrete network design problems for large-scale WDNs, gradient-based mathematical optimization methods are more computationally efficient when dealing with large search spaces associated with continuous variables in optimal network control problems. Considering the advantages of each method, we propose a sequential hybrid method for the optimal design-for-control of large-scale WDNs, where a refinement stage relying on gradient-based mathematical optimization is used to solve continuous optimal control problems corresponding to design solutions returned by an initial EA search. The proposed method is applied to compute the Pareto front of a bi-objective design-for-control problem for the operational network BWPnet, where we consider reopening closed connections between isolated supply areas. The results show that the considered design-for-control strategy increases the resilience of BWPnet while minimizing pressure induced leakage. Moreover, the refinement stage of the proposed hybrid method efficiently improves the coarse approximation computed by the initial EA search, returning a continuous and even Pareto front approximation., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Water Distribution Networks (WDN) are complex and highly interconnected systems. To maintain operation under failure conditions, WDNs should have built-in resilience based on topological and energy redundancy. There are various methods for analysing the resilience of WDNs based on either hydraulic models or surrogate network measures; however, not a single universally accepted method exists. Hydraulic modeling of disruptive operational scenarios suffer from combinatorial restrictions and uncertainties. Methods that rely on surrogate network measures do not take into account the complex interactions between topological and energy redundancy. To bridge this gap, the presented work introduces a hydraulically informed surrogate measure of pipe criticality for the resilience analysis of WDNs, called Water Flow Edge Betweenness Centrality (WFEBC). The WFEBC combines the random walk betweenness centrality with hydraulic (energy) loss principles in pipes. The proposed network resilience estimation method is applied to a case study network and an operational network. Furthermore, a network decomposition approach is proposed to complement the network estimation method and facilitate its scalability to large operational networks. The described resilience analysis method is benchmarked against a hydraulic model-based analysis of WDN reserve capacity. WFEBC is also applied to assess the improvement in resilience allowed by the implementation of a dynamically adaptive topology in an operational network. The benefits and limitations of the proposed method are discussed., Competing Interests: The authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The use of written symbols is a major achievement of human cultural evolution. However, how abstract letter representations might be learned from vision is still an unsolved problem 1,2 . Here, we present a large-scale computational model of letter recognition based on deep neural networks 3,4 , which develops a hierarchy of increasingly more complex internal representations in a completely unsupervised way by fitting a probabilistic, generative model to the visual input 5,6 . In line with the hypothesis that learning written symbols partially recycles pre-existing neuronal circuits for object recognition 7 , earlier processing levels in the model exploit domain-general visual features learned from natural images, while domain-specific features emerge in upstream neurons following exposure to printed letters. We show that these high-level representations can be easily mapped to letter identities even for noise-degraded images, producing accurate simulations of a broad range of empirical findings on letter perception in human observers. Our model shows that by reusing natural visual primitives, learning written symbols only requires limited, domain-specific tuning, supporting the hypothesis that their shape has been culturally selected to match the statistical structure of natural environments 8 .

Performance and injury prevention in elite soccer players are typically investigated from physical-tactical, biomechanical, and metabolic perspectives. However, executive functions, visuospatial abilities, and psychophysiological adaptability or resilience are also fundamental for efficiency and well-being in sports. Based on previous research associating autonomic flexibility with prefrontal cortical control, we designed a novel integrated autonomic biofeedback training method called Neuroplus to improve resilience, visual attention, and injury prevention. Herein, we introduce the method and provide an evaluation of 20 elite soccer players from the Italian Soccer High Division (Serie-A): 10 players trained with Neuroplus and 10 trained with a control treatment. The assessments included psychophysiological stress profiles, a visual search task, and indexes of injury prevention, which were measured pre- and posttreatment. The analysis showed a significant enhancement of physiological adaptability, recovery following stress, visual selective attention, and injury prevention that were specific to the Neuroplus group. Enhancing the interplay between autonomic and cognitive functions through biofeedback may become a key principle for obtaining excellence and well-being in sports. To our knowledge, this is the first evidence that shows improvement in visual selective attention following intense autonomic biofeedback., (© 2017 Society for Psychophysiological Research.)

Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine (RBM), a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual information in the form of internal, distributed representations. We assessed whether this type of network can extract the orthographic structure of English monosyllables by learning a generative model of the letter sequences forming a word training corpus. We show that the network learned an accurate probabilistic model of English graphotactics, which can be used to make predictions about the letter following a given context as well as to autonomously generate high-quality pseudowords. The model was compared to an extended version of simple recurrent networks, augmented with a stochastic process that allows autonomous generation of sequences, and to non-connectionist probabilistic models (n-grams and hidden Markov models). We conclude that sequential RBMs and stochastic simple recurrent networks are promising candidates for modeling cognition in the temporal domain., (Copyright © 2015 Cognitive Science Society, Inc.)

The prefrontal cortex (PFC) supports goal-directed actions and exerts cognitive control over behavior, but the underlying coding and mechanism are heavily debated. We present evidence for the role of goal coding in PFC from two converging perspectives: computational modeling and neuronal-level analysis of monkey data. We show that neural representations of prospective goals emerge by combining a categorization process that extracts relevant behavioral abstractions from the input data and a reward-driven process that selects candidate categories depending on their adaptive value; both forms of learning have a plausible neural implementation in PFC. Our analyses demonstrate a fundamental principle: goal coding represents an efficient solution to cognitive control problems, analogous to efficient coding principles in other (e.g., visual) brain areas. The novel analytical-computational approach is of general interest because it applies to a variety of neurophysiological studies.

Numerical skills have been extensively studied in terms of their development and pathological decline, but whether they change in healthy ageing is not well known. Longer exposure to numbers and quantity-related problems may progressively refine numerical skills, similar to what happens to other cognitive abilities like verbal memory. Alternatively, number skills may be sensitive to ageing, reflecting either a decline of number processing itself or of more auxiliary cognitive abilities that are involved in number tasks. To distinguish between these possibilities we tested 30 older and 30 younger participants on an established numerosity discrimination task requiring to judge which of two sets of items is more numerous, and on arithmetical tasks. Older participants were remarkably accurate in performing arithmetical tasks although their numerosity discrimination (also known as 'number acuity') was impaired. Further analyses indicate that this impairment was limited to numerosity trials requiring inhibiting information incongruent to numerosity (e.g., fewer but larger items), and that this also correlated with poor inhibitory processes measured by standard tests. Therefore, rather than a numerical impairment, poor numerosity discrimination is likely to reflect elderly's impoverished inhibitory processes. This conclusion is supported by simulations with a recent neuro-computational model of numerosity perception, where only the specific degradation of inhibitory processes produced a pattern that closely resembled older participants' performance. Numeracy seems therefore resilient to ageing but it is influenced by the decline of inhibitory processes supporting number performance, consistent with the 'Inhibitory Deficit' Theory., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Deep belief networks hold great promise for the simulation of human cognition because they show how structured and abstract representations may emerge from probabilistic unsupervised learning. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. However, learning in deep networks typically requires big datasets and it can involve millions of connection weights, which implies that simulations on standard computers are unfeasible. Developing realistic, medium-to-large-scale learning models of cognition would therefore seem to require expertise in programing parallel-computing hardware, and this might explain why the use of this promising approach is still largely confined to the machine learning community. Here we show how simulations of deep unsupervised learning can be easily performed on a desktop PC by exploiting the processors of low cost graphic cards (graphic processor units) without any specific programing effort, thanks to the use of high-level programming routines (available in MATLAB or Python). We also show that even an entry-level graphic card can outperform a small high-performance computing cluster in terms of learning time and with no loss of learning quality. We therefore conclude that graphic card implementations pave the way for a widespread use of deep learning among cognitive scientists for modeling cognition and behavior.